Many recent developments in the field of machine learning have focused on the analysis and design of equivariance properties of neural networks.
In many cases, equivariance can be generalized to aribrary homogeneous spaces via group convolution, and these architectures often lead to improved performance when a relevant symmetry of the data can be exploited[1]}, [2]}, [3]}.
